The present invention relates to a method and system for efficiently determining grating profiles using dynamic learning in a library generation process.
Features on semiconductor devices and transmitters of optical fiber links are being formed that are less than one micron in width. Measurement of these sub-micron features is increasingly difficult as the size of the features become smaller. However, knowledge of the dimensions of gratings or periodic structures is essential in order to determine if the dimensions of the features are within the acceptable ranges and if a particular fabrication process causes the sidewalls of the features to be tapered, vertical, T-topped or undercut.
Traditionally, a sample is cleaved and examined with a scanning electron microscope or similar device. This method is slow and expensive. Angular scatterometry has been employed to measure linewidths of gratings, but the process requires a setup of multiple detectors at different angles from the incident beam to measure the diffraction of the scattered light. Again, this is difficult to implement because of the setup required. Another form of angular scatterometry, which only uses zeroth order light, is not very sensitive to sub-100 nm features due to the large wavelength of light typically used by the lasers used in the process.
Spectroscopic reflectometry and ellipsometry are used to direct light on the grating and measure the spectra of reflected signals. Current practices generally use an empirical approach where the spectra of reflected light is measured for a known width of features in a grating. This process is time consuming and expensive even for a limited library of profiles of grating dimensions and the associated spectrum data of reflected light. In another practice, libraries storing large numbers of profiles and signal data need to be built in advance, which requires large upfront processing times and, even then, cover only limited parameter ranges and resolutions. In another practice, real-time regression is used. However, this method covers only a limited parameter range due to the xe2x80x9creal-timexe2x80x9d nature that limits the amount of time available for simulation and search. In addition, unlike the library approach, a strict real-time regression method does not cover the result space comprehensively, potentially leaving the method mired in local minima, versus properly determining the global minimum.
Thus, there is a need for a less laborious and less expensive method of creating the library of profiles and associated spectrum data. There is also a need for a method and system of creating a dynamic library of grating profiles without first generating a master library, thereby obtaining more rapid searches, exhaustive coverage, and limited or zero upfront processing times.
The method and system in accordance with embodiments of the present invention relates to a method of determining an actual grating profile. In an aspect of the invention, an embodiment such as a computer receives a set of measurements, including, for example, reflectivity and change in polarization states, to obtain actual spectrum signal data associated with the grating and generates a first trial profile having a first trial spectrum signal data.
In a further aspect of the invention, the computer compares the first trial spectrum signal data to the actual spectrum signal data. If the first trial spectrum signal data does not match the actual spectrum signal data within preset discrepancy criteria, the computer determines a second trial profile better approximating the actual profile by using optimization techniques, such as local and/or global optimization techniques. The computer iteratively generates additional trial profiles in an attempt to find a trial profile having spectrum signal data matching the actual spectrum signal data. When a match of spectrum signal data within preset discrepancy criteria occurs, the computer retrieves the associated matching trial profile.
In another aspect of the invention, an embodiment provides a method and system for setting up a regression optimization. The embodiment receives a set of measurements and selects values for parameters, parameter ranges, and parameter resolutions. The embodiment may run the regression optimization, generate regression results, analyze the generated regression results, and use the generated regression results to adjust parameters, ranges, and/or resolutions.
In another aspect of the invention, an embodiment provides a method and system for determining a profile associated with a grating by receiving a measured signal, selecting a set of trial parameter values, and determining whether the set of trial parameter values is stored in a database. In a further aspect of the invention, if the set of trial parameter values is stored in the database, an embodiment of the invention searches the database for a trial signal associated with the set of trial parameter values.
In another aspect of the invention, an embodiment provides a method for managing a database by selecting or creating a set of parameters, parameter ranges, and parameter resolutions, storing a set of parameter values to the database, and determining whether all value combinations associated with the set of parameters have been stored into the database.
In another aspect of the invention, an embodiment provides a method and system for determining a profile by receiving a set of measurements associated with an actual signal and searching a profile library for a closest matching set of trial parameter values. The set of trial parameter values may be associated with a trial signal. In a further aspect of the invention, the embodiment determines whether the trial signal satisfies a goodness of fit threshold. If the trial signal satisfies the threshold, an embodiment displays the closest matching set of trial parameter values.